Medical imaging techniques may be characterized as structural or functional. Structural scanning methods such as X-ray based procedures (including Computerized Tomography (CT) scanning) and Magnetic Resonance Imaging (MRI) provide anatomical information about a subject but yield little information concerning biochemical processes or metabolism. Functional techniques such as Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) provide such information by indicating the uptake of a suitably radiolabelled tracer throughout the body of a patient.
To assess a nuclear medicine (NM) scan (PET or SPECT), the clinician needs to have a good understanding of the normal distribution of the radio labelled tracer and the characteristic patterns of uptake from various pathological conditions. The fact that NM images describe function, and not anatomy, adds further difficulty in the assessment as abnormal functional pattern needs to be correlated to any existing abnormal anatomy.
The assessment process involves comparison of the functional scan with the underlying anatomy, which can be obtained from an MRI or CT scan. Software fusion tools or hardware devices can assist in bringing the two images in geometric alignment, but the assessment of the two scans in combination remains the task of the clinician: the exact impact of abnormal anatomy on function is difficult to estimate mentally and the mapping between anatomical information and functional information remains essentially subjective.
The functional scan must also be compared with normal patterns of uptake and known pathological conditions. Other software tools to assist in this step, which have started to appear on the clinical market, compare the patient scan with a database of normal patient scans in order to detect statistically significant abnormalities. This type of comparison has limitations as many individual variations are diluted when comparing with the average of multiple patients. Techniques to try and overcome this problem with methods like partial volume correction alleviate the problem to some extent by trying to model and correct the influence of anatomical variations on the functional uptake. However, they are difficult to interpret as their result depends highly on the quality of the registration between the anatomical scan and the functional scan.
Techniques are known for the simulation of functional scans using data acquired during a structural scan. One such algorithm for PET functional images is PET-SORTEO (A. Reilhac, C Lartizien, N. Costes, S. Sans, C. Comtat, R. N. Gunn and A. C. Evans, “PET-SORTEO: A Monte Carlo-based simulator with high count rate capabilities”, IEEE Trans. Nucl. Sci., vol. 51 no. 1, pp 46-52, February 2004) which is a realistic PET simulator modeling the positron annihilation as they happen in the imaged object, and the detection process by the detectors.
The technique works by:                1) segmenting the structural scan into a number of tissue classes; for example grey-matter, white matter, scalp, cerebro-spinal fluid (CSF) for the brain, or even finer sub-structures of the brain (cortical temporal lobe, parietal lobe, basal ganglia, etc.). (FIG. 2, Step 202)        2) Assigning an individual Time-Activity Curve (TAC), which represents the activity of the tracer uptake as a function of time, to each tissue class. This TAC is modeled from such factors as the tracer itself and tissue type. This is supplied by the user, but a range of normal values could be obtained from experimental protocols. (Step 204)        3) For each class, a series of discrete events is modeled and tracked through to detection in a virtual scanner corresponding to a similar protocol to that used in a real acquired scan. The simulation includes factors such as the scanner type, detector geometry, crystal type, electronic circuit performance, injection-volume of the tracer, etc. (Step 206)        
The results of functional scans (e.g., functional images), such as those acquired using fluorine-18 2-fluoro-2-deoxy-D-glucose-PET (FDG-PET), can be used to determine the drug-uptake in certain regions or the disease state of a certain anatomical region. Often these images cannot be used directly in a quantitative fashion since the tracer uptake depends on a number of factors, such as patient physiology, the equipment used for scanning and amount of biomarker injected. One solution to this problem is first to normalize the scans prior to comparison with a reference of normal uptake.
Normalization typically consists of two steps: the first seeks to adjust the intensity values of the scan to compensate for patient perfusion, metabolism, imaging protocol and scanner variability; the second, registration step, transforms the scan spatially into a common reference coordinate system to compensate for differences between the patient anatomy and that of the average or reference normal.
The reference of normal uptake can be typically generated by applying the steps of normalization to a corpus of normal scans and combining these to generate some kind of average scan.
For example, in the case of assessing FDG-PET scans for assessing Alzheimer's disease, a typical approach is to build a reference average which consists of the mean and standard deviation of a number of Asymptomatic Control (AC) scans which are “normalized” as described above. A patient case scan can then be compared with the reference average for example by computing a score of normality (for instance, a Z-score, or a number of standard deviations) for each voxel, thereby assessing the likelihood of a particular voxel being normal or arising as a result of a disease state.
There are a number of problems associated with this approach:                1) It is often difficult to obtain enough representative AC data from clinical sites (because PET scans are not normally taken from normal, healthy individuals); to build the statistical model, many reference subjects would be needed (more than 30 subjects per class of population (male, female, various age groups)). Even the choice of what is ‘normal’ presents difficulties as this is subjective;        2) A deformable registration step is necessary which maps the novel patient scan to the reference normal space. This never perfectly compensates for individual variations as the images have a limited resolution and deformable registration is a very difficult problem to solve. Errors in the registration may result in different anatomical regions being compared which can lead to significant errors in estimation of the score of normality.        3) The reference average model often does not capture all of the anatomic and functional variation of the AC data, leading to false positives. This is due to the use of over simplistic models used to represent the reference average. For example, some patients may have a bigger cerebellum than the population average; some may have wider Sylvian fissures, etc.        4) Data must be acquired from several sites using different scanners and, or acquisition protocols to avoid the reference average becoming very specific to a particular equipment setting or hospital practice. However, this may lead to a weakening of the reference average and loss of sensitivity of the comparison since these factors are not due to patient variations.        